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master's thesis
Modeli dubokog učenja za procjenu rizika izravnog kreditiranja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:994092
Ucović, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Ucović, I. (2020). Modeli dubokog učenja za procjenu rizika izravnog kreditiranja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:994092

Ucović, Ivan. "Modeli dubokog učenja za procjenu rizika izravnog kreditiranja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:994092

Ucović, Ivan. "Modeli dubokog učenja za procjenu rizika izravnog kreditiranja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:994092

Ucović, I. (2020). 'Modeli dubokog učenja za procjenu rizika izravnog kreditiranja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:994092

Ucović I. Modeli dubokog učenja za procjenu rizika izravnog kreditiranja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 August 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:994092

I. Ucović, "Modeli dubokog učenja za procjenu rizika izravnog kreditiranja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:994092

Metadata
TitleModeli dubokog učenja za procjenu rizika izravnog kreditiranja
Title (english)Deep Learning Models for Risk Assessment in Peer-to-Peer Lending
AuthorIvan Ucović
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
Committee memberJuraj Petrović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Modeli dubokog učenja za procjenu rizika izravnog kreditiranja U ovom radu razvijeni su modeli strojnog i dubokog učenja za predikciju kreditnog rizika izravnog ili P2P kreditiranja. Naime izravno kreditiranje nije tako novi trend u financijskoj industriji, međutim jedan je od najbrže rastućih u istoj. Ovakvo kreditiranje sadrži rizike za kreditore, koji posuđuju vlastiti novac, pa se pojavljuje sve veća potreba za kvalitetnim modelima procjene kreditnog rizika. U ovom radu razvijeni su modeli koji koristeći podatke koji su dostupni kreditorima prije odobravanje kredita, diskriminiraju dobre od loših kredita. Modeli su evaluirani nizom metrika koje su standard u industriji.
Abstract (english)
Deep models for credit risk assessment in peer to peer financing. In this paper, three models for credit risk assessment were created using machine and deep learning techniques. Peer to peer financing is not that new, but it is the fastest growing industry in fintech. This type of crediting involves huge risks for investors since they are borowing their own money. This poses a new need for quality credit risk assessment models. Models in this paper were created using dana available to investors prior to credit acceptance and were trained to differerntiate between good and bad credits. At the end these models were evaluated by roc-auc metric, as a standard metric in this industry as well as some other know metrics.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:994092
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 14:06:51